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CIKM-2020.md

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CIKM-2020

CTR Prediction

  1. Deep Time-Aware Item Evolution Network for Click-Through Rate Prediction
  2. MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
  3. Deep Multi-Interest Network for Click-through Rate Prediction
  4. MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
  5. 【阿里】Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-through Rate Prediction
  6. Dimension Relation Modeling for Click-Through Rate Prediction
  7. 【华为】AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction
  8. 【华为】Ensembled CTR Prediction via Knowledge Distillation
  9. Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions

GNN based Recommendation

  1. TGCN: Tag Graph Convolutional Network for Tag-Aware Recommendation
  2. Star Graph Neural Networks for Session-based Recommendation
  3. DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
  4. Multiplex Graph Neural Networks for Multi-behavior Recommendation
  5. Time-aware Graph Relational Attention Network for Stock Recommendation
  6. GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems [华为]
  7. Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation
  8. Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items

Knowledge Graph-enhanced Recommendation

  1. News Recommendation with Topic-Enriched Knowledge Graphs
  2. Multi-modal Knowledge Graphs for Recommender Systems
  3. CAFE: Coarse-to-Fine Knowledge Graph Reasoning for E-Commerce Recommendation
  4. MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings

Sequential Recommendation

  1. Hybrid Sequential Recommender via Time-aware Attentive Memory Network
  2. Improving End-to-End Sequential Recommendations with Intent-aware Diversification
  3. Quaternion-based self-Attentive Long Short-term User Preference Encoding for Recommendation
  4. Star Graph Neural Networks for Session-based Recommendation
  5. S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
  6. DynamicRec: A Dynamic Convolutional Network for Next Item Recommendation

Text-based Recommendation

  1. Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation
  2. TPR: Text-aware Preference Ranking for Recommender Systems
  3. News Recommendation with Topic-Enriched Knowledge Graphs
  4. Transformer Models for Recommending Related Questions in Web Search
  5. ArXivDigest: A Living Lab for Personalized Scientific Literature Recommendation

Job Recommendation

  1. Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
  2. Learning Effective Representations for Person-Job Fit by Feature Fusion

Location-based Recommendation

  1. STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation
  2. Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation
  3. Magellan: A Personalized Travel Recommendation System Using Transaction Data

Social Recommendation

  1. Partial Relationship Aware Influence Diffusion via Multi-channel Encoding Scheme for Social Recommendation
  2. DREAM: A Dynamic Relation-Aware Model for social recommendation

Ranking/Re-ranking

  1. Personalized Re-ranking with Item Relationships for E-commerce
  2. Personalized Flight Itinerary Ranking at Fliggy
  3. Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
  4. 【华为】U-rank: Utility-oriented Learning to Rank with Implicit Feedback
  5. E-commerce Recommendation with Weighted Expected Utility

Recommendation Diversity

  1. ART (Attractive Recommendation Tailor): How the Diversity of Product Recommendations Affects Customer Purchase Preference in Fashion Industry?
  2. P-Companion: A Principled Framework for Diversified Complementary Product Recommendation

Explainable Recommendation

  1. Explainable Recommender Systems via Resolving Learning Representations
  2. Generating Neural Template Explanations for Recommendation

User Profiling

  1. Ranking User Attributes for Fast Candidate Selection in Recommendation Systems
  2. Learning to Build User-tag Profile in Recommendation System
  3. Masked-field Pre-training for User Intent Prediction

Online Advertising

  1. Representative Negative Instance Generation for Online Ad Targeting
  2. Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement
  3. A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

Knowledge Distillation for Recommendation

  1. DE-RRD: A Knowledge Distillation Framework for Recommender System
  2. 【华为】Ensembled CTR Prediction via Knowledge Distillation

AutoML for Recommendation

  1. 【华为】AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction

RL for Recommendation

  1. Whole-Chain Recommendations

Cold-Start and Transfer Learning

  1. Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
  2. Dual Autoencoder Network with Swap Reconstruction for Cold-Start Recommendation
  3. Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

Debiasing

  1. Feedback Loop and Bias Amplification in Recommender Systems
  2. Exploring Missing Interactions: A Convolutional Generative Adversarial Network for Collaborative Filtering

Evaluation

  1. Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems
  2. Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
  3. LensKit for Python: Next-Generation Software for Recommender Systems Experiments

New Scenarios

  1. 【阿里】EdgeRec: Recommender System on Edge in Mobile Taobao
  2. Leveraging Historical Interaction Data for Improving Conversational Recommender System

Others

  1. Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization
  2. Personalized Bundle Recommendation in Online Game
  3. Selecting Influential Features by a Learnable Content-Aware Linear Threshold Model
  4. Attacking Recommender Systems with Augmented User Profiles